The underbalanced drilling has been widely used due to its advantages of high drilling efficiency and low cost etc., especially for hard formation drilling. These advantages, however, are closely related to the stress...The underbalanced drilling has been widely used due to its advantages of high drilling efficiency and low cost etc., especially for hard formation drilling. These advantages, however, are closely related to the stress state of the bottom-hole rock; therefore, it is significant to research the stress distribution of bottom-hole rock for the correct understanding of the mechanism of rock fragmentation and high penetration rate. The stress condition of bottom-hole rock is very complicated while under the co-action of overburden pressure, horizontal in-situ stresses, drilling mud pressure, pore pressure and temperature etc. In this paper, the fully coupled simulation model is established and the effects of overburden pressure, horizontal in-situ stresses, drilling mud pressure, pore pressure and temperature on stress distribution of bottom-hole rock are studied. The research shows that: in air drilling, as the well depth increases, the more easily the bottom-hole rock is broken; the mud pressure has a great effect on the bottom hole rock. The bigger the mud pressure is, the more difficult to break the bottom-hole rock; the max principle stress of the bottom-hole increased with the increasing of mud pressure, well depth and temperature difference. The bottom-hole rock can be divided into 3 regions respectively according to the stress state, 3 direction stretch zone, 2 direction compression area and 3 direction compression zone; the corresponding fragmentation degree of difficulty is easily, normally and hardly.展开更多
The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are used.This is because the empirica...The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are used.This is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental datasets.In addition,most machine learning(ML)FBHP prediction models were constructed with real-time well data points and published without any visible mathematical equation.This makes it difficult for other readers to use these ML models since the datasets used in their development are not open-source.This study presents a white-box adaptive neuro-fuzzy inference system(ANFIS)model for real-time prediction of multiphase FBHP in wellbores.1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi eSugeno fuzzy inference systems(FIS)structures.The dataset was divided into two sets;80%for training and 20%for testing.Statistical performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction performance.The optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter sets.Trend analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on FBHP.In addition,graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models,empirical correlations,and machine learning models.New training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.展开更多
Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic mo...Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.展开更多
基金Projects(U1562212,51525404)supported by the National Natural Science Foundation of ChinaProject(JYBFX-YQ-1)supported by the Research Project of Key Laboratory Machinery and Power Machinery(Xihua University),Ministry of Education,China
文摘The underbalanced drilling has been widely used due to its advantages of high drilling efficiency and low cost etc., especially for hard formation drilling. These advantages, however, are closely related to the stress state of the bottom-hole rock; therefore, it is significant to research the stress distribution of bottom-hole rock for the correct understanding of the mechanism of rock fragmentation and high penetration rate. The stress condition of bottom-hole rock is very complicated while under the co-action of overburden pressure, horizontal in-situ stresses, drilling mud pressure, pore pressure and temperature etc. In this paper, the fully coupled simulation model is established and the effects of overburden pressure, horizontal in-situ stresses, drilling mud pressure, pore pressure and temperature on stress distribution of bottom-hole rock are studied. The research shows that: in air drilling, as the well depth increases, the more easily the bottom-hole rock is broken; the mud pressure has a great effect on the bottom hole rock. The bigger the mud pressure is, the more difficult to break the bottom-hole rock; the max principle stress of the bottom-hole increased with the increasing of mud pressure, well depth and temperature difference. The bottom-hole rock can be divided into 3 regions respectively according to the stress state, 3 direction stretch zone, 2 direction compression area and 3 direction compression zone; the corresponding fragmentation degree of difficulty is easily, normally and hardly.
文摘The majority of published empirical correlations and mechanistic models are unable to provide accurate flowing bottom-hole pressure(FBHP)predictions when real-time field well data are used.This is because the empirical correlations and the empirical closure correlations for the mechanistic models were developed with experimental datasets.In addition,most machine learning(ML)FBHP prediction models were constructed with real-time well data points and published without any visible mathematical equation.This makes it difficult for other readers to use these ML models since the datasets used in their development are not open-source.This study presents a white-box adaptive neuro-fuzzy inference system(ANFIS)model for real-time prediction of multiphase FBHP in wellbores.1001 real well data points and 1001 normalized well data points were used in constructing twenty-eight different Takagi eSugeno fuzzy inference systems(FIS)structures.The dataset was divided into two sets;80%for training and 20%for testing.Statistical performance analysis showed that a FIS with a 0.3 range of influence and trained with a normalized dataset achieved the best FBHP prediction performance.The optimal ANFIS black-box model was then translated into the ANFIS white-box model with the Gaussian input and the linear output membership functions and the extracted tuned premise and consequence parameter sets.Trend analysis revealed that the novel ANFIS model correctly simulates the anticipated effect of input parameters on FBHP.In addition,graphical and statistical error analyses revealed that the novel ANFIS model performed better than published mechanistic models,empirical correlations,and machine learning models.New training datasets covering wider input parameter ranges should be added to the original training dataset to improve the model's range of applicability and accuracy.
文摘Accurate prediction of multiphase flowing bottom-hole pressure(FBHP)in wellbores is an important factor required for optimal tubing design and production optimization.Existing empirical correlations and mechanistic models provide inaccurate FBHP predictions when applied to real-time field datasets because they were developed with laboratory-dependent parameters.Most machine learning(ML)models for FBHP prediction are developed with real-time field data but presented as black-box models.In addition,these ML models cannot be reproduced by other users because the dataset used for training the machine learning algorithm is not open source.These make using the ML models on new datasets difficult.This study presents an artificial neural network(ANN)visible mathematical model for real-time multiphase FBHP prediction in wellbores.A total of 1001 normalized real-time field data points were first used in developing an ANN black-box model.The data points were randomly divided into three different sets;70%for training,15%for validation,and the remaining 15%for testing.Statistical analysis showed that using the Levenberg-Marquardt training optimization algorithm(trainlm),hyperbolic tangent activation function(tansig),and three hidden layers with 20,15 and 15 neurons in the first,second and third hidden layers respectively achieved the best performance.The trained ANN model was then translated into an ANN visible mathematical model by extracting the tuned weights and biases.Trend analysis shows that the new model produced the expected effects of physical attributes on FBHP.Furthermore,statistical and graphical error analysis results show that the new model outperformed existing empirical correlations,mechanistic models,and an ANN white-box model.Training of the ANN on a larger dataset containing new data points covering a wider range of each input parameter can broaden the applicability domain of the proposed ANN visible mathematical model.